This application pertains to camera based analysis of road traffic. An existing issue is to detect a moving vehicle, measure its speed, and track the vehicle to capture an image of the vehicle's license plate. This is desired to be done from a mobile camera platform which is nominally an Unmanned Aerial Vehicle, however it is to be appreciated that other camera platforms are considered as well.
Multiple ground-based technologies are available for detecting traffic law violations such as speeding, unsafe lane changes, running red lights, illegal turns, etc. Human operated speed detection is commonly accomplished by following an offending vehicle through vehicle-mounted and hand-held radar. In another area automated technology exists for detecting red-light running. This may use a combination of road sensors and cameras affixed to poles near an intersection. Automated speed violation detection may also be accomplished by using a combination of radar and fixed cameras, or by cameras alone if calibrated.
Air-based detection of unsafe driving and especially speeding is done using human visual monitoring from an aircraft. A suspect vehicle must be tracked for sufficient distance to establish speed by the timing of the vehicle passing calibrated landmarks.
Modern autonomous unmanned aircraft routinely can navigate via Global Positioning System (GPS). UAVs are routinely fitted with still and video cameras that are capable of recording images and transmitting live video feeds to ground stations.
The University of Florida has investigated traffic monitoring with high end military UAVs, however, they were focused on traffic density measurements and not enforcement related activities.
Military UAVs have been equipped with pan/tilt cameras.
Various computer vision algorithms have been developed for appearance-based tracking of objects in scenes. These algorithms do not detect moving objects directly. They require initialization of the target object, and once the target object has been initialized, the target object is tracked through time. An example of such a concept is discussed in Zdenek Kalal, K. Mikolajaczyk, and J. Matas, “Tracking-Learning-Detection,” Pattern Analysis and Machine Intelligence, 2011.
Various computer vision algorithms have been developed for detecting and tracking multiple moving objects in scenes of traffic from a stationary platform, where the object(s) to be tracked are determined automatically by their motion relative to the fixed scene. An example of the foregoing concepts is discussed in S. J. Pundlik and S. T. Birchfield, “Motion Segmentation at Any Speed,” Proceedings of the British Machine Vision Conference (BMVC), Edinburgh, Scotland, pages 427-436, September 2006.